There is a need to determine and quantify global change induced phenological asynchrony because of possible loss of biodiversity and implications for the food web. Phenological asynchrony in freshwater lakes can be studied retrospectively by analysing historical data, and prospectively by scenario analysis based on historical data. Models allowing to study phenology by scenario analysis need to be valid for multiple populations over decades limiting the suitability of process-based models that are structural rigid and calibratable only for a limited period of time.

Here we applied models inferred from historical data by evolutionary computation to simulate food-web dynamics of the plankton community of Lake Müggelsee from 2002 to 2012. The models were driven by nutrient concentrations, water temperature (WT) and endogenous interrelationships within the plankton community. The validated models simulated seasonal and inter-annual dynamics of seven phyto- and zooplankton groups in response to scenarios of prospective global warming, nutrient enrichments as well as combinations of warming with nutrient enrichments and warming with nutrient reductions.

Phenological WT-sensitivities resulting from the warming scenario indicated substantial shifts towards earlier timing of cyanobacteria peaks and delayed timing of cladocerans peaks in summer, suggesting significant phenological asynchrony in the plankton community of Lake Müggelsee. The phenological sensitivities of cyanobacteria and cladocerans towards phosphorus and nitrogen enrichments revealed similar trends of summer peaks as identified for warming, most likely contributing to phenological asynchrony.

The combination of warming and nutrient reductions showed increased spring maxima but almost unchanged summer peaks of cyanobacteria demonstrating that gradually decreasing phosphorus and nitrogen concentrations may outweigh warming effects on phytoplankton growth.

Scenarios that simulated WT- and nutrient-changes as gradual processes rather than immediately imposed events proved to be more realistic and credible. The proposed ensemble of complementary inferential models proved to be a viable tool for determining long-term dynamics and phenological asynchronies in plankton communities under the impact global changes.

由于生物多样性可能丧失和对食物网的影响，需要确定和量化全球变化引起的物候异步。淡水湖泊物候不同步可以通过分析历史数据进行回顾性研究，也可以通过基于历史数据的情景分析进行前瞻性研究。允许通过情景分析来研究物候学的模型需要在几十年内对多个种群有效，这限制了基于过程的模型的适用性，这些模型结构刚性且仅在有限的时间内可校准。

在这里，我们应用通过进化计算从历史数据中推断出的模型来模拟 2002 年至 2012 年 Müggelsee 湖浮游生物群落的食物网动态。这些模型由营养浓度、水温 (WT) 和浮游生物群落内的内源性相互关系驱动。经验证的模型模拟了七个浮游植物和浮游动物群的季节性和年际动态，以响应预期的全球变暖、营养丰富以及变暖与营养丰富和变暖与营养减少的组合。

变暖情景导致的物候 WT 敏感性表明，夏季蓝藻高峰的时间提前和枝角类动物高峰的时间延迟，这表明 Müggelsee 湖浮游生物群落存在显着的物候异步。蓝藻和枝角类动物对磷和氮富集的物候敏感性揭示了夏季峰值的相似趋势，如为变暖而确定的，最有可能导致物候不同步。

变暖和养分减少的结合显示出春季最大值增加，但蓝藻的夏季峰值几乎没有变化，这表明逐渐降低的磷和氮浓度可能超过变暖对浮游植物生长的影响。

将 WT 和养分变化模拟为渐进过程而不是立即强加事件的场景被证明更现实和可信。所提议的互补推理模型集合被证明是一种可行的工具，可用于确定受全球变化影响的浮游生物群落的长期动态和物候异步。